Interference chromatography: a novel approach to optimizing chromatographic selectivity and separation performance for virus purification
Bibliographic record
Abstract
BACKGROUND: Oncolytic viruses are playing an increasingly important role in cancer immunotherapy applications. Given the preclinical and clinical efficacy of these virus-based therapeutics, there is a need for fast, simple, and inexpensive downstream processing methodologies to purify biologically active viral agents that meet the increasingly higher safety standards stipulated by regulatory authorities like the Food and Drug Administration and the European Agency for the Evaluation of Medicinal Products. However, the production of virus materials for clinical dosing of oncolytic virotherapies is currently limited-in quantity, quality, and timeliness-by current purification technologies. Adsorption of virus particles to solid phases provides a convenient and practical choice for large-scale fractionation and recovery of viruses from cell and media contaminants. Indeed, chromatography has been deemed the most promising technology for large-scale purification of viruses for biomedical applications. The implementation of new chromatography media has improved process performance, but low yields and long processing times required to reach the desired purity are still limiting. RESULTS: Here we report the development of an interference chromatography-based process for purifying high titer, clinical grade oncolytic Newcastle disease virus using NatriFlo® HD-Q membrane technology. This novel approach to optimizing chromatographic performance utilizes differences in molecular bonding interactions to achieve high purity in a single ion exchange step. CONCLUSIONS: When used in conjunction with membrane chromatography, this high yield method based on interference chromatography has the potential to deliver efficient, scalable processes to enable viable production of oncolytic virotherapies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".